In one embodiment, a traffic inspection service executed by an intermediary device obtains, from a monitoring agent executed by an endpoint device, keying information for an encrypted traffic session between the endpoint device and a remote entity. The traffic inspection service provides a notification to the monitoring agent that acknowledges receipt of the keying information. The traffic inspection service uses the keying information to decrypt encrypted traffic from the encrypted traffic session. The traffic inspection service applies a policy to the encrypted traffic session between the endpoint device and the remote entity, based on the decrypted traffic from the session.
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11. A system, comprising:
a monitoring agent installed on an endpoint device, the monitoring agent operative to:
monitor a memory space of the endpoint device associated with an application or a dedicated process hosted by the endpoint device;
detect from the memory space, by the monitoring agent, a handshake initiated between an application hosted on the endpoint device and a remote entity, the handshake initiated to establish an encrypted traffic session between the application and the remote entity;
capture from the memory space of the endpoint device, by the monitoring agent, session key information for the encrypted traffic session between the endpoint device and the remote entity, wherein session keys are established during the handshake and are obtained after the encrypted traffic session is formed; and
transmit the session key information to a traffic inspection service hosted on an intermediary device located between the endpoint device and the remote entity over a secure connection;
the traffic inspection service operative to:
use the session key information to decrypt encrypted traffic from the encrypted traffic session, resulting in decrypted traffic;
apply, based on the decrypted traffic comprising cleartext from the encrypted traffic session, a policy to the encrypted traffic session between the endpoint device and the remote entity to determine whether the decrypted traffic is malicious or against the policy; and
perform a mitigation action in response to a determination that the decrypted traffic is malicious or against the policy.
1. A method, comprising:
monitoring, by a monitoring agent executing on an endpoint device, a memory space of the endpoint device associated with an application or a dedicated process hosted by the endpoint device;
detecting from the memory space, by the monitoring agent, a handshake initiated between an application hosted on the endpoint device and a remote entity, the handshake initiated to establish an encrypted traffic session between the application and the remote entity;
capturing from the memory space of the endpoint device, by the monitoring agent, session key information for the encrypted traffic session between the endpoint device and the remote entity, wherein session keys are established during the handshake and are obtained after the encrypted traffic session is formed;
transmitting the session key information to a traffic inspection service hosted on an intermediary device located between the endpoint device and the remote entity over a secure connection;
using, by the traffic inspection service, the session key information to decrypt encrypted traffic from the encrypted traffic session, resulting in decrypted traffic;
applying, by the traffic inspection service and based on the decrypted traffic comprising cleartext from the encrypted traffic session, a policy to the encrypted traffic session between the endpoint device and the remote entity to determine whether the decrypted traffic is malicious or against the policy; and
performing, by the traffic inspection service, a mitigation action in response to a determination that the decrypted traffic is malicious or against the policy.
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This application is a continuation of U.S. patent application Ser. No. 18/416,439, filed on Jan. 18, 2024, which is a continuation of U.S. patent application Ser. No. 17/722,131, filed on Apr. 15, 2022, which is a continuation of U.S. patent application Ser. No. 16/100,361, filed on Aug. 10, 2018, all entitled ENDPOINT-ASSISTED INSPECTION OF ENCRYPTED NETWORK TRAFFIC, by Rehak et al., the entire contents of which are incorporated by reference herein.
The present disclosure relates generally to computer networks, and, more particularly, to endpoint-assisted inspection of encrypted network traffic.
Enterprise networks are carrying a very fast growing volume of both business and non-business critical traffic. Often, business applications such as video collaboration, cloud applications, etc., use the same hypertext transfer protocol (HTTP) and/or HTTP secure (HTTPS) techniques that are used by non-business critical web traffic.
Beyond the various types of legitimate application traffic in a network, some network traffic may also be malicious. For example, some traffic may seek to exfiltrate sensitive information from a network, such as credit card numbers, trade secrets, and the like. Further types of malicious network traffic include network traffic that propagate the malware itself and network traffic that passes control commands to already infected devices, such as in the case of a distributed denial of service (DDOS) attack.
Inspection of network traffic is relatively straight-forward, when the network traffic is unencrypted. For example, techniques such as deep packet inspection (DPI), allows a networking device to inspect the payloads of packets and identify the contents of the packets. However, the use of traffic encryption is becoming increasingly ubiquitous
Many instances of malware now use encryption, to conceal their network activity from detection. Beyond even the case of malware infection, the use of encrypted web traffic is becoming increasingly ubiquitous, making techniques such as DPI unable to identify threats and prevent sensitive data from being communicated elsewhere.
The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which:
According to one or more embodiments of the disclosure, a traffic inspection service executed by an intermediary device obtains, from a monitoring agent executed by an endpoint device, keying information for an encrypted traffic session between the endpoint device and a remote entity. The traffic inspection service provides a notification to the monitoring agent that acknowledges receipt of the keying information. The traffic inspection service uses the keying information to decrypt encrypted traffic from the encrypted traffic session. The traffic inspection service applies a policy to the encrypted traffic session between the endpoint device and the remote entity, based on the decrypted traffic from the session.
In further embodiments, a monitoring agent of a device detects a handshake between the device and a remote entity to form an encrypted traffic session. The monitoring agent captures keying information for the session from the handshake. The monitoring agent provides the captured keying information to a traffic inspection service. The traffic inspection service uses the keying information to decrypt encrypted traffic from the encrypted traffic session and applies a policy to the traffic session based on the decrypted traffic. The monitoring agent delays providing of the encrypted traffic from the device to the remote entity, until the traffic inspection service acknowledges receipt of the provided keying information.
A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers and workstations, or other devices, such as sensors, etc. Many types of networks are available, with the types ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, and others. The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol consists of a set of rules defining how the nodes interact with each other. Computer networks may further be interconnected by an intermediate network node, such as a router, to extend the effective “size” of each network.
Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or “AMI” applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform any other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port, a microcontroller, and an energy source, such as a battery. Often, smart object networks are considered field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth.
In some implementations, a router or a set of routers may be connected to a private network (e.g., dedicated leased lines, an optical network, etc.) or a virtual private network (VPN), such as an MPLS VPN, thanks to a carrier network, via one or more links exhibiting very different network and service level agreement characteristics. For the sake of illustration, a given customer site may fall under any of the following categories:
1.) Site Type A: a site connected to the network (e.g., via a private or VPN link) using a single CE router and a single link, with potentially a backup link (e.g., a 3G/4G/LTE backup connection). For example, a particular CE router 110 shown in network 100 may support a given customer site, potentially also with a backup link, such as a wireless connection.
2.) Site Type B: a site connected to the network using two MPLS VPN links (e.g., from different service providers), with potentially a backup link (e.g., a 3G/4G/LTE connection). A site of type B may itself be of different types:
2a.) Site Type B1: a site connected to the network using two MPLS VPN links (e.g., from different service providers), with potentially a backup link (e.g., a 3G/4G/LTE connection).
2b.) Site Type B2: a site connected to the network using one MPLS VPN link and one link connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/LTE connection). For example, a particular customer site may be connected to network 100 via PE-3 and via a separate Internet connection, potentially also with a wireless backup link.
2c.) Site Type B3: a site connected to the network using two links connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/LTE connection).
Notably, MPLS VPN links are usually tied to a committed service level agreement, whereas Internet links may either have no service level agreement at all or a loose service level agreement (e.g., a “Gold Package” Internet service connection that guarantees a certain level of performance to a customer site).
3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but with more than one CE router (e.g., a first CE router connected to one link while a second CE router is connected to the other link), and potentially a backup link (e.g., a wireless 3G/4G/LTE backup link). For example, a particular customer site may include a first CE router 110 connected to PE-2 and a second CE router 110 connected to PE-3.
Servers 152-154 may include, in various embodiments, a network management server (NMS), a dynamic host configuration protocol (DHCP) server, a constrained application protocol (CoAP) server, an outage management system (OMS), an application policy infrastructure controller (APIC), an application server, etc. As would be appreciated, network 100 may include any number of local networks, data centers, cloud environments, devices/nodes, servers, etc.
The techniques herein may also be applied to other network topologies and configurations. For example, the techniques herein may be applied to peering points with high-speed links, data centers, etc. Further, in various embodiments, network 100 may include one or more mesh networks, such as an Internet of Things network. Loosely, the term “Internet of Things” or “IoT” refers to uniquely identifiable objects/things and their virtual representations in a network-based architecture. In particular, the next frontier in the evolution of the Internet is the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, heating, ventilating, and air-conditioning (HVAC), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., via IP), which may be the public Internet or a private network.
Notably, shared-media mesh networks, such as wireless networks, etc., are often on what is referred to as Low-Power and Lossy Networks (LLNs), which are a class of network in which both the routers and their interconnect are constrained. In particular, LLN routers typically operate with highly constrained resources, e.g., processing power, memory, and/or energy (battery), and their interconnections are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen to thousands or even millions of LLN routers, and support point-to-point traffic (e.g., between devices inside the LLN), point-to-multipoint traffic (e.g., from a central control point such at the root node to a subset of devices inside the LLN), and multipoint-to-point traffic (e.g., from devices inside the LLN towards a central control point). Often, an IoT network is implemented with an LLN-like architecture. For example, as shown, local network 160 may be an LLN in which CE-2 operates as a root node for nodes/devices 10-16 in the local mesh, in some embodiments.
The network interfaces 210 include the mechanical, electrical, and signaling circuitry for communicating data over physical links coupled to the network 100. The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Notably, a physical network interface 210 may also be used to implement one or more virtual network interfaces, such as for virtual private network (VPN) access, known to those skilled in the art.
The memory 240 comprises a plurality of storage locations that are addressable by the processor(s) 220 and the network interfaces 210 for storing software programs and data structures associated with the embodiments described herein. The processor 220 may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures 245. An operating system 242 (e.g., the Internetworking Operating System, or IOS®, of Cisco Systems, Inc., another operating system, etc.), portions of which are typically resident in memory 240 and executed by the processor(s), functionally organizes the node by, inter alia, invoking network operations in support of software processors and/or services executing on the device. These software processors and/or services may comprise a traffic analysis process 248.
It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that processes may be routines or modules within other processes.
In general, traffic analysis process 248 may execute one or more machine learning-based classifiers to classify encrypted traffic in the network (and its originating application) for any number of purposes. In one embodiment, traffic analysis process 248 may assess captured telemetry data regarding one or more traffic flows, to determine whether a given traffic flow or set of flows are caused by malware in the network, such as a particular family of malware applications. Example forms of traffic that can be caused by malware may include, but are not limited to, traffic flows reporting exfiltrated data to a remote entity, spyware or ransomware-related flows, command and control (C2) traffic that oversees the operation of the deployed malware, traffic that is part of a network attack, such as a zero day attack or denial of service (DOS) attack, combinations thereof, or the like. In further embodiments, traffic analysis process 248 may classify the gathered telemetry data to detect other anomalous behaviors (e.g., malfunctioning devices, misconfigured devices, etc.), traffic pattern changes (e.g., a group of hosts begin sending significantly more or less traffic), or the like.
Traffic analysis process 248 may employ any number of machine learning techniques, to classify the gathered telemetry data. In general, machine learning is concerned with the design and the development of techniques that receive empirical data as input (e.g., telemetry data regarding traffic in the network) and recognize complex patterns in the input data. For example, some machine learning techniques use an underlying model M, whose parameters are optimized for minimizing the cost function associated to M, given the input data. For instance, in the context of classification, the model M may be a straight line that separates the data into two classes (e.g., labels) such that M=a*x+b*y+c and the cost function is a function of the number of misclassified points. The learning process then operates by adjusting the parameters a,b,c such that the number of misclassified points is minimal. After this optimization/learning phase, traffic analysis process 248 can use the model M to classify new data points, such as information regarding new traffic flows in the network. Often, M is a statistical model, and the cost function is inversely proportional to the likelihood of M, given the input data.
In various embodiments, traffic analysis process 248 may employ one or more supervised, unsupervised, or semi-supervised machine learning models. Generally, supervised learning entails the use of a training set of data, as noted above, that is used to train the model to apply labels to the input data. For example, the training data may include sample telemetry data that is “normal,” or “malware-generated.” On the other end of the spectrum are unsupervised techniques that do not require a training set of labels. Notably, while a supervised learning model may look for previously seen attack patterns that have been labeled as such, an unsupervised model may instead look to whether there are sudden changes in the behavior of the network traffic. Semi-supervised learning models take a middle ground approach that uses a greatly reduced set of labeled training data.
Example machine learning techniques that traffic analysis process 248 can employ may include, but are not limited to, nearest neighbor (NN) techniques (e.g., k-NN models, replicator NN models, etc.), statistical techniques (e.g., Bayesian networks, etc.), clustering techniques (e.g., k-means, mean-shift, etc.), neural networks (e.g., reservoir networks, artificial neural networks, etc.), support vector machines (SVMs), logistic or other regression, Markov models or chains, principal component analysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP) ANNs (e.g., for non-linear models), replicating reservoir networks (e.g., for non-linear models, typically for time series), random forest classification, or the like.
The performance of a machine learning model can be evaluated in a number of ways based on the number of true positives, false positives, true negatives, and/or false negatives of the model. For example, the false positives of the model may refer to the number of traffic flows that are incorrectly classified as malware-generated, anomalous, etc. Conversely, the false negatives of the model may refer to the number of traffic flows that the model incorrectly classifies as normal, when actually malware-generated, anomalous, etc. True negatives and positives may refer to the number of traffic flows that the model correctly classifies as normal or malware-generated, etc., respectively. Related to these measurements are the concepts of recall and precision. Generally, recall refers to the ratio of true positives to the sum of true positives and false negatives, which quantifies the sensitivity of the model. Similarly, precision refers to the ratio of true positives the sum of true and false positives.
In some cases, traffic analysis process 248 may assess the captured telemetry data on a per-flow basis. In other embodiments, traffic analysis process 248 may assess telemetry data for a plurality of traffic flows based on any number of different conditions. For example, traffic flows may be grouped based on their sources, destinations, temporal characteristics (e.g., flows that occur around the same time, etc.), combinations thereof, or based on any other set of flow characteristics.
As shown in
In some embodiments, a networking device may analyze packet headers, to capture feature information about the traffic flow. For example, router CE-2 may capture the source address and/or port of host node 10, the destination address and/or port of server 154, the protocol(s) used by packet 302, or other header information by analyzing the header of a packet 302. Example captured features may include, but are not limited to, Transport Layer Security (TLS) information (e.g., from a TLS handshake), such as the ciphersuite offered, user agent, TLS extensions (e.g., type of encryption used, the encryption key exchange mechanism, the encryption authentication type, etc.), HTTP information (e.g., URI, etc.), Domain Name System (DNS) information, or any other data features that can be extracted from the observed traffic flow(s).
In further embodiments, the device may also assess the payload of the packet to capture information about the traffic flow. For example, router CE-2 or another device may perform deep packet inspection (DPI) on one or more of packets 302, to assess the contents of the packet. Doing so may, for example, yield additional information that can be used to determine the application associated with the traffic flow (e.g., packets 302 were sent by a web browser of node 10, packets 302 were sent by a videoconferencing application, etc.). However, as would be appreciated, a traffic flow may also be encrypted, thus preventing the device from assessing the actual payload of the packet. In such cases, the characteristics of the application can instead be inferred from the captured header information.
The networking device that captures the flow telemetry data may also compute any number of statistics or metrics regarding the traffic flow. For example, CE-2 may determine the start time, end time, duration, packet size(s), the distribution of bytes within a flow, etc., associated with the traffic flow by observing packets 302. In further examples, the capturing device may capture sequence of packet lengths and time (SPLT) data regarding the traffic flow, sequence of application lengths and time (SALT) data regarding the traffic flow, or byte distribution (BD) data regarding the traffic flow.
As noted above, encrypted network traffic can be used to conceal the spread of malware infections, malware command and control (C&C) signaling, data exfiltration, and other malicious activities. Notably, encryption protects the packet payloads from being inspected by techniques such as DPI, making it difficult to distinguish between benign traffic and malicious traffic.
One approach to dealing with the potential threat of encrypted traffic is to perform man-in-the-middle traffic inspection. Under this approach, an intermediate proxy between the communicating endpoints essentially acts as each endpoint from the standpoint of the other. More specifically, when one endpoint requests a secure connection with another endpoint, the proxy captures this request and sends its own request on to the other endpoint. In doing so, each endpoint essentially thinks that it is communicating securely with the other endpoint, but is actually communicating instead with the proxy. This allows the proxy to decrypt the traffic and perform packet inspection on the traffic.
A man-in-the-middle proxy approach is not viable in all circumstances. Indeed, there are privacy considerations that may prevent the decryption of all encrypted traffic. Moreover, man-in-the-middle inspection of encrypted traffic becomes very hard with TLS 1.3, as the use of Diffie-Hellmann authenticated handshakes prevents the inspection for legitimate and malicious content alike. While this provides end-to-end confidentiality and integrity protection, it also prevents any effective security inspection of the content.
Endpoint-Assisted Inspection of Encrypted Network Traffic
The techniques herein allow for the inspection of encrypted network traffic, including traffic that leverages Diffie-Hellman handshake-generated keying information, such as in TLS 1.3. In some aspects, the techniques herein interface endpoint security software (e.g., a monitoring agent) with an intermediary traffic inspection service, such as a network-based firewall. During operation, the endpoint software obtains the session encryption keys used by the communicating process and provides them to the traffic inspection service. In turn, the service is able to decrypt the traffic and inspect the contents of the communication, without having to resort to a man-in-the-middle approach.
Specifically, according to one or more embodiments of the disclosure as described in detail below, a traffic inspection service executed by an intermediary device obtains, from a monitoring agent executed by an endpoint device, keying information for an encrypted traffic session between the endpoint device and a remote entity. The traffic inspection service provides a notification to the monitoring agent that acknowledges receipt of the keying information. The traffic inspection service uses the keying information to decrypt encrypted traffic from the encrypted traffic session. The traffic inspection service applies a policy to the encrypted traffic session between the endpoint device and the remote entity, based on the decrypted traffic from the session.
Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the traffic analysis process 248, which may include computer executable instructions executed by the processor 220 (or independent processor of interfaces 210) to perform functions relating to the techniques described herein.
Operationally, the techniques herein combine an endpoint security program/monitoring agent and an intermediary traffic inspection service (e.g., a firewall service, etc.), whereby the endpoint agent retrieves the symmetric encryption keys from the process on the endpoint device that opens the secure connection with a remote entity, communicates them to the intermediate firewall and the firewall uses the keys to inspect the content of the traffic encrypted by the keys.
Situated along the communication path between endpoint device 402 and remote entity 406 may be one or more intermediary devices that implement a traffic inspection service 404 such as a traditional firewall, next generation firewall, intrusion detection system (IDS), or the like. For example, in some implementations, traffic inspection service 404 may be located at an edge of a local network that also includes endpoint device 402, thereby allowing traffic inspection service 404 to inspect any traffic flowing into, or out of, the local network.
In various embodiments, a key component of the techniques herein is a monitoring agent 410 that is executed by endpoint device 402. Such a monitoring agent may be based on an existing endpoint security product, such as Cisco's Advanced Malware Protection (AMP), other security products, or may be a custom agent specifically designed to perform the techniques herein. Depending on the configuration of monitoring agent 410, it may monitor the files, processes/applications, various memory spaces, and/or process trees of endpoint device 402. For example, in some embodiments, monitoring agent 410 may leverage this information to determine whether a given file, process, or process tree is suspected of being malicious. In further embodiments, monitoring agent 410 may not make these types of security assessments, but may simply be an agent on endpoint device 402 that monitors the health or resource consumption of endpoint device 402.
During operation, monitoring agent 410 may use its root-level privileges to retrieve the (ephemeral) symmetric encryption keys used by the communicating process to encode the content of the communication over a secure connection (e.g., TLS, SSH, etc.). For example, assume that application 408 on endpoint device 402 initiates a handshake between endpoint device 402 and remote entity 406 to form an encrypted traffic session via key exchange 412. In such cases, monitoring agent 410 will retrieve the keying information from key exchange 412 when they are available (e.g., after completion of the Diffie-Hellman handshake that happens upon the connection setup). Note that the keying information may not be directly stored in the memory space of the communicating process, such as application 408, but may be stored within another process or dedicated system process (e.g. lsass, etc.), in further embodiments. The principle would still remain the same, though, and monitoring agent 410 may retrieve the keying information from the corresponding memory space of endpoint device 402, as appropriate.
According to various embodiments, monitoring agent 410 may only capture the keying information, or may only send the captured keying information, under certain circumstances. These circumstances may include, but are not limited to, any or all of the following:
As shown in
To protect keying information 414 during transit, mutual authentication between endpoint device 402 and traffic inspection service 404 may be used. In some cases, the mutual authentication may be cloud-driven, but the session keys/keying information 414 obtained by monitoring agent 410 must not go to the cloud. Or, if keying information 414 transits through the cloud, it may do so through an encrypted channel with keys of this channel not accessible to the cloud. In other words, keying information 414 must be protected inside a secure channel between endpoint device 402 and traffic inspection service 404 (e.g., firewall, proxy, IDS, etc.).
As shown in
Traffic inspection service 404 may apply any number of inspection rules to the cleartext of the decrypted traffic 416. For example, service 404 may apply a set of IDS rules to the cleartext and/or header information of traffic 416, to determine whether traffic 416 is malicious or suspected of being malicious. In another embodiment, service 404 may apply a privacy policy to the decrypted traffic 416. For example, if the cleartext includes personally identifiable information, a file that has been flagged as restricted, trade secret information, or the like, traffic inspection service 404 may similarly deem traffic 416 as a violation.
Note that the rules applied by traffic inspection service 404 to traffic 416 may be privacy-preserving (e.g., the rules must not leak arbitrary data about traffic unrelated to any attack). In some cases, traffic inspection service 416 may be configured so as not to allow a user to write specific rules with the intent to obtain specific information from a legitimate connection. In order to achieve this, service 404 may impose a restriction of only applying the rules written by a trusted third party (e.g., Cisco Talos, etc.) and not customer-specific rules. Traffic inspection service 404 may also store and use the keying information only in a protected enclave and guarantee that the decrypted plaintext of traffic 416 never leaves the protected enclave in any other form than the inspection results. Traffic inspection service 404 may also use morphisec memory protection, to make it impossible for other processes to retrieve the stored keys, in some embodiments.
In response to determining that traffic 416 has violated a security or privacy policy, traffic inspection service 404 may take any number of mitigation actions with respect to traffic 416. For example, in some cases, service 404 may cause traffic 416 to be dropped or rerouted, to effectively terminate the session between endpoint device 402 and remote entity 406. In further cases, traffic inspection service 404 may capture information about traffic 416 for purposes of sending an alert to a security expert, user of endpoint device 402, or another interested party. Captured traffic information about traffic 416 can also be used, in some cases, for purposes of training a machine learning model to assess network traffic.
To ensure that traffic inspection service 404 inspects all encrypted traffic, various approaches can be employed. In one embodiment, monitoring agent 410 on endpoint device 402 may delay the transmission of the first packet of encrypted traffic 416 until traffic inspection service 404 sends a notification back to monitoring agent 410 confirming receipt of keying information 414 by traffic inspection service 404. In further cases, the delay can be implemented directly as part of traffic inspection service 404, such as by dropping packets of encrypted traffic 416 until keying information 414 is received and relying on TCP retransmission to recover the packet. In further embodiments, traffic inspection service 404 may, by policy, block all encrypted traffic for which keying information has not been received by service 404 for any or all endpoint devices under its purview.
At step 515, as detailed above, the monitoring agent may capture keying information for the session from the handshake. In various embodiments, the agent may obtain the keys from the memory space of the communicating application or, in some cases, of another dedicated system process (e.g., lsass, etc.) used to facilitate the handshake.
At step 520, the monitoring agent may provide the captured keying information to a traffic inspection service, as described in greater detail above. In various embodiments, the traffic inspection service uses the keying information to decrypt encrypted traffic from the encrypted traffic session and applies a policy to the traffic session based on the decrypted traffic. In some embodiments, the monitoring agent may only provide the keying information to the traffic inspection service, or only capture the keying information, if the process, process tree, and/or file associated with the traffic session.
At step 525, as detailed above, the monitoring agent may delay providing of the encrypted traffic from the device to the remote entity, until the traffic inspection service acknowledges receipt of the sent keying information. In other words, the agent may prevent the device from sending the encrypted traffic towards the remote entity until the inspection service is able to decrypt the traffic for inspection. Alternatively, the traffic inspection service may be configured to drop traffic unless it has already received the corresponding keying information for the traffic. Procedure 500 then ends at step 530.
At step 615, as detailed above, the traffic inspection service may provide a notification to the monitoring agent that acknowledges receipt of the keying information. In some embodiments, the monitoring agent may be configured to prevent the endpoint device from sending packets via the encrypted traffic session until receipt of the keying information by the inspection service is acknowledged.
At step 620, the traffic inspection service may use the keying information to decrypt encrypted traffic from the encrypted traffic session, as described in greater detail above. In other words, the monitoring agent on the endpoint device may send the appropriate information to the traffic inspection service, to allow the service to decrypt the encrypted traffic of the session, thereby exposing the cleartext of the communication to the service.
At step 625, as detailed above, the traffic inspection service may apply a policy to the encrypted traffic session between the endpoint device and the remote entity, based on the decrypted traffic from the session. For example, the service may apply a security and/or data privacy policy to the traffic session, to determine whether the traffic is malicious (e.g., C&C traffic, malware propagation, etc.) and/or against privacy policy (e.g., conveys sensitive data, etc.). If a policy violation exists, the service may take any number of mitigation actions, such as blocking the session (e.g., dropping traffic from the encrypted session, etc.), generating alerts, or the like. Procedure 600 then ends at step 630.
It should be noted that while certain steps within procedures 500-600 may be optional as described above, the steps shown in
The techniques described herein, therefore, allow for the analysis of the contents of encrypted network traffic without having to employ a man-in-the-middle approach. In TLS 1.3, for example, such a man-in-the-middle approach is no long feasible due to the use of Diffie-Hellman handshake authentication.
While there have been shown and described illustrative embodiments that provide for endpoint-assisted inspection of encrypted network traffic, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, while certain embodiments are described herein with respect to using certain models for purposes of traffic analysis, the models are not limited as such and may be used for other functions, in other embodiments. In addition, while certain protocols are shown, such as TLS and SSH, other suitable protocols may be used, accordingly.
The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly, this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein.
McGrew, David, Anderson, Blake Harrell, Rehak, Martin, Dunlop, Scott William
| Patent | Priority | Assignee | Title |
| Patent | Priority | Assignee | Title |
| 8856910, | Aug 31 2011 | PALO ALTO NETWORKS, INC | Detecting encrypted tunneling traffic |
| 9203931, | Apr 01 2013 | Amazon Technologies, Inc | Proxy server testing |
| 9338147, | Apr 24 2015 | ExtraHop Networks, Inc. | Secure communication secret sharing |
| 9843593, | Aug 31 2011 | Palo Alto Networks, Inc. | Detecting encrypted tunneling traffic |
| 9967292, | Oct 25 2017 | ExtraHop Networks, Inc.; EXTRAHOP NETWORKS, INC | Inline secret sharing |
| 20030128695, | |||
| 20030191963, | |||
| 20050251859, | |||
| 20100158009, | |||
| 20140115702, | |||
| 20150312267, | |||
| 20160309331, | |||
| 20170289109, | |||
| 20180026986, | |||
| 20190068561, | |||
| 20190349403, | |||
| JP2017068677, |
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